The present invention is directed to a system and method for reconstruction of images from cone beam volume computed tomography (CBVCT) and more particularly to such a system and method in which the data are taken over an orbit having a circle and two or more arcs.
Among all possible applications of the Radon transform, computed tomography (CT) applied in 2-D medical and non-destructive test imaging technology may be the one that has achieved the greatest success. Recognizing the demand for saving scan time in the currently available 2-D CT and consequently greatly improving its functionality, the implementation of CBVCT has been investigated for the past two decades.
The intermediate function derived by Grangeat (P. Grangeat, xe2x80x9cMathematical Framework of Cone Beam 3D Reconstruction via the First Derivative of the Radon Transform,xe2x80x9d Mathematical Methods in Tomography, Lecture Notes in Mathematics 1497, G. T. Herman et al, eds., New York: Springer Verlag, 1991, pp. 66-97) establishes a bridge between the projection of a 3-D object and its 3-D Radon transform and is much more numerically tractable than previously known intermediate functions. With the progress in understanding the so-called data sufficiency condition for an exact reconstruction, a few cone beam non-planar scanning orbits, such as dual orthogonal circles, helical, orthogonal circle-and-line, non-orthogonal dual-ellipse, orthogonal circle-plus-arc, and even general vertex path have been proposed. Correspondingly, the analytic algorithms to exactly reconstruct a 3-D object based upon those non-planar scanning orbits have also been presented.
Generally, a cone beam filtered back-projection (FBP) algorithm can make cone beam reconstruction much more computationally efficient and more easily implemented in a multi-processor parallel computing structure. Hence, an FBP cone beam reconstruction algorithm is desirable in practice, and Feldkamp""s algorithm (L. A. Feldkamp, L. C. Davis, and J. W. Kress, xe2x80x9cPractical cone-beam algorithm,xe2x80x9d J. Opt. Soc. Am. A, Vol. 1, pp. 612-619, 1984) for the circular orbit is the earliest example. Obviously, Feldkamp""s algorithm violates the data sufficiency condition, and an accurate reconstruction without intrinsic artifacts is available only in the central plane overlapping the circular orbit plane, so that some accuracy on the off-central planes has to be sacrificed. Although proposed independently, many algorithms of the prior art featured a common structure of shift variant filtering (SVF) followed by cone beam back-projection. Only 1-D ramp filtering is employed in Feldkamp""s algorithm, but a cascade of 2-D operations, such as weighting, 2-D projection, differentiation and 2-D back-projection, are involved in the shift variant filtering. The complexity of the SVF (O(N4) ) is higher than that of the 1-D ramp filtering of Feldkamp""s algorithm (O(N3logN) ). Another important common feature possessed by many algorithms of the prior art is a normalized redundancy function (NRF) adopted to compensate for the multiple intersections of the projection plane with the source trajectory. Recently, that kind of algorithm has been extended to a more general situation in which an arbitrary vertex path is involved as long as the data sufficiency condition is satisfied. Apparently, the NRF is data-acquisition-orbit-dependent and has discontinuities in data acquisition orbits which meet the data sufficiency condition, but it can be analytically calculated for either a specific data acquisition orbit or even an arbitrary vertex path. On the other hand, the algorithm by Hu (H. Hu, xe2x80x9cA new cone beam reconstruction algorithm for the circle-and-line orbit,xe2x80x9d Proceedings of International Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine, pp. 303-310, 1995; and H. Hu, xe2x80x9cExact regional reconstruction of longitudinally-unbounded objects using the circle-and-line cone beam tomographic system,xe2x80x9d Proc. SPIE, Vol. 3032, pp. 441-444, 1997) for an orthogonal circle-plus-line orbit is promising in saving computation resource, since a window function, instead of the NRF, is employed for the cone beam reconstruction from the projection data acquired along the line orbit.
Due to mechanical feasibility, a circular x-ray source trajectory is still the dominant data acquisition geometry in all commercial 2-D/3-D CT systems currently available. Based upon a circular source trajectory, a number of data acquisition orbits can be implemented by either moving the table or tilting the CT gantry. An orthogonal circle-plus-arc orbit has been presented. It possesses advantages that can not be superseded by other xe2x80x9ccircle-plusxe2x80x9d geometries, especially in the application of image guided interventional procedures requiring intraoperative imaging, in which the movement of a patient table is to be avoided. Further, it can be easily realized on a C-arm-based imaging system, which is being used more and more for tomography in recent years. The orthogonal circle-plus-arc orbit can be realized by acquiring one set of 2-D cone beam circle projections while rotating an x-ray source and a 2D detector on a circular gantry and then acquiring another set of 2-D cone beam arc projections while tilting the gantry along an arc which is orthogonal to and coincident with the circular orbit at the same radius. The exact CBVCT reconstruction algorithm associated with that circle-plus-arc orbit is not in the FBP form. The rebinning process involved in the algorithm requires storage for all information in the Radon space, and makes the CBVCT reconstruction computationally inefficient. Further, the arc sub-orbit provides information covering its Radon sub-domain only once, but the circular sub-orbit provides information covering its Radon sub-domain twice. That unbalanced coverage in the Radon space may result in non-uniformity of noise characteristic in reconstructed images.
A particular application of the present invention is in the detection of lung cancer and other malignancies. CT scanning plays a central role in much of the thoracic imaging used in detection of lung cancer and other malignancies. CT is non-invasive, easy to perform, and usually straightforward to interpret. It is either the primary modality or the referral modality for the detection of pulmonary masses (primary and metastatic), non-invasive staging of primary bronchogenic carcinoma, and for detection of major complications of malignancies, particularly pulmonary emboli, and infections. However, present helical CT has three major technical shortcomings. First, helical CT scans require a long or multiple breath holds for whole lung imaging, depending on slice thickness. Second, slice thickness vs. coverage vs. scan time tradeoff: programming thinner slices increases scan time or decreases coverage. The spatial resolution is not isotropic; through plane resolution is limited by slice thickness and a few times lower than that of in-plane. Third, the clinically achievable in-plane resolution for a large FOV, such as whole lung imaging, is limited and less than or equal to 1.0 lp/mm.
CT of the chest is a potential screening tool for lung carcinoma. While screening programs based on conventional x-rays had poor sensitivity and diagnosed most carcinomas after the window of surgical cure had passed, CT scans reveal nodules below 1 centimeter with higher potential cure rates. A drawback of screening CT is poor specificity. Benign sub-centimeter nodules are common (non-calcified granulomas, intrapulmonary lymph nodes, focal regions of atelectasis). The best diagnostic algorithm post-discovery of sub-centimeter nodules is unclear. Universal resection seems impractical. Potential diagnostic algorithms include evaluating the nodule enhancement, border characteristics, and growth. In all of these cases, accurate depiction of a small nodule is necessary. Helical CT, while readily detecting these nodules, has partial volume averaging problems in accurate characterization. It would therefore be desirable to provide a scanning system and method with sub-millimeter isotropic resolution, which would potentially better characterize the density and size of these small nodules. Accurate size measurement would allow short-term follow-up to evaluate for growth.
While CT screening for bronchogenic carcinoma in the high-risk population may or may not be clinically beneficial and economically practical, chest CT for the detection of metastases is commonly performed. CT is performed at the time of initial diagnosis, as interval monitoring for detection of disease, and as follow-up of detected nodules which are not initially resected. In all cases, improved detection and characterization, particularly that of interval growth, should be clinically beneficial.
Three image intensifier (II)-based cone beam reconstructions for volume lung imaging have been reported before. However, all II-based CBVCT for volume lung imaging suffers from inaccurate reconstruction due to the use of a single circle cone beam acquisition geometry and its corresponding approximating algorithm by Feldkamp et al, in addition to a limited performance of the II-CCD imaging chain. The best low contrast detectability of the II-based cone beam CT for volume lung imaging is 10 HUs for a 3 mm object.
It will be readily apparent from the above that a need exists in the art to overcome the above-noted limitations of the prior art. It is therefore an object of the invention to satisfy the data sufficiency condition while achieving a more balanced coverage. It is another object of the invention to do so in a computationally efficient manner which can be adapted to parallel cone beam reconstruction.
To achieve the above and other objects, the present invention is directed to a system and method for reconstructing images from data taken over a circle and two or more arcs. An FBP reconstruction algorithm is presented for reconstructing the images.
The efficiency of reconstruction is critical for the application of CBVCT in the image-guided interventional procedures, and the reconstructed images with uniform noise characteristic are desired in practice. In order to overcome the previously mentioned shortcomings of the circle-plus-arc orbit and its associated Radon Transform-based reconstruction algorithm, a circle-plus-two-arc orbit and an analytic FBP cone bean reconstruction algorithm are used. The result given by Hu for the circular cone beam projections is directly incorporated. For the cone beam projections acquired along the arc orbits (namely, arc cone beam projections), originating from the equation established by Grangeat and the inverse Radon transform, an analytic reconstruction solution is obtained. That solution is different from known solutions because a window function, instead of an NRF, is employed to compensate for the multiple intersections of the projection plane with the x-ray source trajectory. Since its support in the Radon domain is very limited, the window function of the present invention significantly reduces the computational cost of the reconstruction from the arc CB projections.
Most objects to be reconstructed in medical or non-destructive x-ray CT are longitudinally unbounded. Hence, a cone beam reconstruction algorithm should address such a truncation problem. In order to solve the so-called truncated cone beam projection, several methods have been proposed. It has been demonstrated that a finite region of interest (ROI), for which the extended data sufficiency condition is satisfied, can be reconstructed accurately, although that finite ROI is slightly smaller than the ROI which can be scanned by a detector. The circle-plus-two-arc orbit and its associated cone beam FBP reconstruction algorithm in the present invention are intrinsically capable of dealing with the truncation problem, and its thorough evaluation is accomplished herein.
The circle-plus-arcs orbit possesses advantages over other xe2x80x9ccircle-plusxe2x80x9d orbits for the application of x-ray CBVCT in image-guided interventional procedures requiring intraoperative imaging, in which movement of the patient table is to be avoided. A cone beam circle-plus-two-arc orbit satisfying the data sufficiency condition and a filtered back-projection (FBP) algorithm to reconstruct longitudinally unbounded objects is presented here. In the circle sub-orbit, the algorithm employs Feldkamp""s formula and another FBP implementation. In the arc sub-orbits, an FBP solution is obtained originating from Grangeat""s formula, and the reconstruction computation is significantly reduced using a window function to exclude redundancy in Radon domain. The algorithm""s merits include the following: Only 1-D filtering is implemented even in a 3-D reconstruction, only separable 2-D interpolation is required to accomplish the 3-D back projection, and the algorithm structure is appropriate for parallel computation.
The present invention has the following characteristics and advantages. A flat panel detector (FPD) can be used. The invention can incorporate scattering correction and volume-of-interest (VOI) reconstruction. The present invention can be used for medical imaging, nondestructive testing or any other purpose in which such imaging is desired.
In the reconstruction algorithm of the preferred embodiment, all the components are in a filtered backprojection format. That reconstruction algorithm is more computationally efficient than those of the prior art and is ready for parallel cone beam reconstruction. That algorithm can be used to provide an exact reconstruction of a longitudinally unbounded object. The CBVCT reconstruction of the preferred embodiment is the 3D matrix of attenuation coefficient distribution of a 3D object.
In the present invention, the data are taken through a scan such as a quasi-spiral scan. To achieve the fastest scan, a simplified scan, such as only tilt in plus circle scan, can be used to satisfy the data sufficiency condition. The second set of arc projection scans (gantry tilt-out scans) is optional to improve image quality. The total acquisition time can be reduced by decreasing the sampling rate on the arcs or by using only a gantry-tilt-in plus a circle scan during the quasi-spiral scan.
The present invention offers the following particular advantages when used to detect lung cancer. First, the present invention requires a much shorter volume scanning time relative to helical CT. In a single volume scan, an entire acquisition can be performed. The present invention can improve acquisition efficiency by a factor of 25 for 1 mm slice thickness per volume scan vs. a single ring helical CT. Assuming a 25 cm segment to be scanned for a whole lung imaging and 1 mm/slice, the present invention can be at least 24 times faster than a single ring detector helical CT and 3 (for gantries with 0.5 sec./revolution) to 6 times faster than a multi-ring detector helical CT. The fast volume scan eliminates the respiratory misregistration problems, such as those caused by the requirement that the patient hold his or her breath, and is less sensitive to patient motion.
Second, the present invention can provide isotropic resolution in the x, y and z directions and provide true 3D reconstruction images. The spatial resolution of FPD-based CBVCT is limited by the fineness of our detector array, not by collimation. An FPD-based CBVCT achieves spatial resolution on the order of 1-2 lp/mm in routine mode. The present invention can provide higher resolution in all three directions than a helical CT.
Third, the embodiment with ultra-high resolution VOI reconstruction can provide true 3D tomographic reconstruction with spatial resolution approaching that of screen-film projection imaging, but with 50-100 times better contrast resolution than projection imaging. This spatial resolution capability cannot be achieved in any current helical CT.
In addition, the present invention can more efficiently use x-ray tube output and greatly reduce the tube loading requirement. This will reduce the manufacture cost of CT tubes because a very heavy duty and very costly x-ray CT tube ($60,000-$100,000/tube) may not be needed, and/or the operating cost because the life of a CT tube will be many times longer.
The present invention thus improves the sensitivity and specificity of lung cancer detection as well as other types of cancer detection. In addition, it will highly significant to the early detection and management, not only of lung cancer, but also of other malignancies.
There are several radiological or biological characteristics of carcinoma that can be imaged. First, carcinoma has different x-ray linear attenuation coefficients from surrounding tissues. Second, carcinoma has a substantially higher volume growth rate compared to a benign tumor, which lacks growth. Third, carcinoma has border patterns distinguishable from those of a benign tumor. Fourth, benign tumors show different contrast enhancement after intravenous contrast injection. Fifth, the presence of neovascularity can indicate cancer. Conventional cancer detection techniques such as chest projection imaging rely mainly on the first characteristic and partially use the third characteristic for cancer detection. Since mammography is a two-dimensional static imaging technique, it cannot provide any information regarding characteristics 2, 4, or 5. The present invention, by allowing fast scans and permitting the use of contrast injection if desired, can be used to detect cancers in accordance with all five characteristics.
CT scanning is a key modality for detecting pulmonary malignancies. It can detect lesions as small 2-mm diameter. It is, however, imperfect for detection of nodules for the following reasons:
Nodules may not be imaged if the lungs cannot be scanned in a single breathhold. Respiratory misregistration occurs when a CT scan of the lungs is acquired in several different breath holds. Because patients do not reliably hold their breath in the same phase of respiration, and because pulmonary lesions move cranially or caudally with respiration, a CT scan composed of slices obtained from different breath holds may fail to detect a lesion because that lesion was never imaged. The present invention permits scanning the entire lungs in a single breathhold and thus can eliminate this source of detection error.
Nodules may be present on the CT images but fail to be recognized by the interpreting radiologist. A retrospective review of nine patients with missed lung cancer on CT found five missed tumors that were peripheral and  less than 3 mm in diameter and four central tumors measuring up to 8 mm in diameter. These small peripheral nodules were likely not seen while the larger central nodules were not recognized set against the background of the larger complex branching vessels. Review of a CT dataset electronically, and in planes other than the axial plane might also prove to have further increase in sensitivity for nodule detection. The present invention provides the first system capable of scanning the entire chest with sub-millimeter isotropic resolution. Isotropic resolution with sub-millimeter resolution in all directions would be ideally suited for electronic interpretation in axial, oblique, coronal and sagittal planes.
Partial volume averaging with adjacent lung can make small pulmonary nodules difficult or impossible to detect by helical CT. Helical CT of canine metastatic osteosaroma found 44% of metastases xe2x89xa65 mm vs. 91% of metastases  greater than 5 mm. Usually, helical CT reconstructs images at an interval approximately equal to the collimation, 5-7 mm. Some clinically relevant nodules are smaller than the slice thickness. Reconstructing images at a smaller reconstruction interval increases the sensitivity for lung nodule detection. This is due to the non-linear slice sensitivity profile of helical CT reconstruction. These overlapping reconstructions have a better chance of placing small nodules in the center of the slice where they will be displayed with higher density and be more easily seen. The present invention can overcome this problem because slices at  less than 1 mm thick would essentially eliminate partial volume averaging and also assure that a nodule larger than 3 mm would have a slice through its approximate center.
Nodule size is difficult to measure accurately by helical CT. The apparent size of a pulmonary nodule depends on the thickness of the slice and where the slice is reconstructed relative to the nodule. Accurate size measurements of the nodules are necessary to detect small amounts of growth in short-term follow-ups. A 3 mm diameter nodule growing to 4 mm diameter has more than doubled in volume. Because the detection of small nodules is becoming increasingly common due to helical CT, it is likely that imaging algorithms will need to incorporate follow-up of small nodules for growth. Accurate sizing would be essential. CBVCT will provide 0.125-0.7 mm voxel size and will allow accurate measurements of nodule size and nodule volume.
Small nodule density (attenuation coefficient) is difficult to measure accurately by helical CT. The apparent density of a pulmonary nodule in helical CT depends on the position of the nodule relative to the position of the reconstructed slice. The relative movement of the slice by one or two mm may make a calcified nodule appear non-calcified. For nodules smaller than the slice thickness (routinely 5-10 mm in helical CT), there is partial volume averaging of the nodule with adjacent air and an accurate density can not be determined. The nodule density is useful for characterization in two major respects. One is the detection of calcification indicating benignity. The second is that malignant pulmonary nodules appear to have more rapid contrast enhancement than benign nodules. Sub-millimeter thick slices, achieved by CBVCT, will allow accurate density measurements of small nodules without partial volume averaging and without necessity for post-processed overlapping reconstructions. This should better detect calcification, and more accurately characterize the amount of enhancement.
Fine spiculations and other nodule border characteristics are best determined with high resolution CT. On helical CT scanners, this requires locating the nodule prospectively, as it is impractical to acquire high-resolution CT 1-mm thick slices throughout the entire lungs. CBVCT would acquire high-resolution images through every nodule without prior knowledge of its location or the need for technologists or physician localization during the scan. The ultra-high resolution VOI reconstruction mode of CBVCT will provide even higher resolution for target imaging after the survey lung imaging of CBVCT with lower resolution. This mode may be even more useful for characterizing nodule border. The value of universal high resolution CT for characterizing benign vs. malignant nodules may also prove beneficial.
A particular implementation of CBVCT provides high contrast resolution of 0.7-4 lp/mm, and low contrast detectability of 3-5 CT number within a short breath hold (2-8 seconds). Such an implementation preferably includes an appropriate 2D detector system which has a high detection quantum efficiency (DQE), high dynamic range, high spatial resolution, minimal geometric distortion, and which is capable of high image acquisition rates with little image lag and excellent linearity. It also preferably includes a data acquisition scheme that will result in a complete set of projection data with little additional mechanical complexity. This provides an exact cone beam reconstruction algorithm which is based on the complete set of data, thereby permitting imaging in a large FOV (for example 14xe2x80x3-16xe2x80x3). A third aspect which is preferably included is x-ray scatter control and correction techniques to further improve low contrast detectability.
The present invention expands the application of CBVCT from angiography to volume lung imaging and other applications that require soft tissue differentiation. CBVCT can potentially be applied to pulmonary emboli detection, liver cancer detection, volumetric brain perfusion, diagnosis of acute stroke, and colon cancer detection, etc.
For lung cancer and other malignancies, the present invention has application to malignancy detection, monitoring, management and treatment and in particular to the development of treatment plans.